Delhi Pharmaceutical Science and Research University

Cyclodextrins (CDs) are cyclic oligosaccharides composed of α-(1,4)-linked glucopyranose units that have emerged as multifunctional and versatile pharmaceutical excipients. One of the major challenges in modern drug development is that nearly half of newly discovered drug molecules exhibit poor aqueous solubility, which adversely affects formulation development, bioavailability, and therapeutic efficacy. CDs address this limitation by forming non-covalent inclusion and non-inclusion complexes, thereby enhancing drug solubility, stability, dissolution rate, and overall biopharmaceutical performance. This review provides a comprehensive overview of CDs, including their historical background, structural characteristics, and production through starch conversion by the enzyme cyclodextrin glucanotransferase (CGTase). Special emphasis is placed on the transglycosylation reactions catalyzed by CGTase, including cyclization, coupling, and disproportionation, which play a critical role in CD synthesis. Recent advances in structural elucidation techniques, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, molecular dynamics simulations, and ion mobility mass spectrometry, are also discussed. The pharmaceutical applications of CDs are critically evaluated, with particular emphasis on their roles as solubility enhancers, taste-masking agents, and stabilizers in nanocarrier-based and targeted drug delivery systems. Their applications in cosmetics and dermopharmaceuticals are also explored, particularly in improving formulation stability and enabling controlled drug delivery. Furthermore, the pharmacokinetics, toxicological safety, and regulatory acceptability of various CDs are discussed. Overall, this review highlights the growing importance of CDs as pharmaceutical excipients that bridge supramolecular chemistry and advanced drug delivery systems. Illustration of (a) production of CDs from starch via transglycosylation reactions catalyzed by CGTase through four reaction mechanisms, i.e., disproportion, coupling, cyclization, and hydrolysis, (b) their pharmaceutical, targeted delivery, and dermopharmaceutical applications are also illustrated here.

INTRODUCTION:BBB limits the permeability of external compounds by 98% to maintain and regulate brain homeostasis. Hence, BBB permeability prediction is vital to predict the activity of a drug-like substance. AIM:Neuronal disorders have affected more than 15% of the world's population, signifying the importance of continued design and development of drugs that can cross the Blood-Brain Barrier (BBB). OBJECTIVE:Here, we report about developing BBBper (Blood-Brain Barrier permeability prediction) using machine learning tool. METHODS:A supervised machine learning-based online tool, based on physicochemical parameters to predict the BBB permeability of given chemical compounds was developed. The user-end webpage was developed in HTML and linked with back-end server by a python script to run user queries and results. RESULTS:BBBper uses a random forest algorithm at the back end, showing 97% accuracy on the external dataset, compared to 70-92% accuracy of currently available web-based BBB permeability prediction tools. CONCLUSION:The BBBper web tool is freely available at http://bbbper.mdu.ac.in.

Developing novel pharmacological compounds for disease treatment is an inherently time-consuming and costly process, yet research continues unabated. Leveraging existing data resources and identifying innovative therapeutic leads are critical steps in drug design. The integration of artificial intelligence (AI) and machine learning (ML) offers powerful tools for designing and developing translational nanomedicines. The biological activity of a nanomedicine is largely determined by its physicochemical properties, including size, shape, surface charge, and chemical composition. These properties can be systematically optimized using nanoinformatics approaches, such as quantitative structure-activity/property relationship (QSAR/QSPR) models, enabling enhanced functionality of engineered nanomedicines while minimizing potential health and environmental risks during development. Physiologically based pharmacokinetic (PBPK) models further complement these approaches by predicting drug and nanomedicine distribution in body fluids, extrapolating experimental data, and establishing correlations between physicochemical properties and biodistribution. Such models are particularly valuable for toxicity assessment. This review focuses on the implementation of nanoinformatics tools and AI to facilitate the translation of nanomedicines from bench to clinic. Computational strategies for designing nanodelivery systems are highlighted, including selecting suitable nanomaterials, assessing potential nanotoxicity, and developing simulation models for in vitro and in vivo analyses. Additionally, the review examines the contributions of AI and ML to the development of translational nanomedicines, as well as the associated challenges and future research directions. The compiled insights are highly relevant to research groups involved in drug discovery, nanotechnology, and the development of advanced drug delivery systems for biomedical applications. Importantly, the methodologies discussed have broad applicability across multiple scientific disciplines.

Bergenia ciliata a himalayan medicinal herb, which has been traditionally used due to its extensive pharmacological properties. Nevertheless, the possible anticancer application on the molecular level has not been fully explored. This experiment was developed to determine the phytochemicals of B. ciliata as natural inhibitors of the epidermal growth factor receptor (EGFR), which is a major target in most epithelial cancers. Phytochemical data on the IMPPAT and PubChem databases were collected. The compounds were using SwissADME and ProTox-II on drug-likeness, absorption, and toxicity. Then six candidates with Lipinski rule and pharmacokinetics conditions were docked to EGFR (PDB ID: 4HJO) with AutoDock vina. The binding affinities of cianidanol and Leucocianidol were the highest, − 8.8 kcal/mol and − 8.7 kcal/mol respectively, as compared to the reference drug erlotinib which has a binding affinity of − 8.3 kcal/Mol. There were several hydrogen bonds and hydrophobic interactions with such critical residues as Lys_721, Thr_766, Asp_831 and Phe_832.Simulations of 100 ns of molecular dynamics showed constant RMSD (0.10–0.20 nm), low fluctuations of residues, and small radius of gyration of all the complexes. MM/PBSA required interactions revealed that the stabilization was dominated by van der Waals forces and electrostatic repulsions with the total binding energies of − 51 kJ/mol, − 46 kJ/mol, and − 34 kJ/mol with cianidanol, leucocianidol, and erlotinib respectively. The studies suggests that EGFR is strongly bound by B. ciliata phytochemicals, and their biocompatible profiles are safer and more inclined to biocompatibility compared to the conventional inhibitor. These findings have indicated that these compounds can be useful lead scaffolds in the development of anticancer drugs in future as they have been shown to possess promising properties that would be further validated by studies conducted in in vitro and in vivo.

Vulvovaginal candidiasis (VVC) is a prevalent fungal infection with high recurrence, antifungal resistance, and poor patient compliance. Terbinafine’s antifungal activity is enhanced by tea tree, neem, and citronella oils, which disrupt fungal membranes, inhibit biofilms, and reduce inflammation. Incorporating these oils into a nanoemulsion improves penetration, sustains release, and enhances efficacy. However, terbinafine hydrochloride’s poor solubility and bioavailability limit its therapeutic potential. This study aimed to develop a terbinafine hydrochloride-loaded nanoemulsion gel for VVC treatment using natural antifungal oils and a Quality by Design (QbD) approach. A QbD-based strategy was used to formulate and optimize a stable nanoemulsion gel with enhanced solubility, controlled release, and improved bioavailability. Spontaneous emulsification was followed by optimization using a Central Composite Rotatable Design (CCRD)-based Design of Experiments (DoE). The optimized 1 QbD strategy used to optimize terbinafine hydrochloride nanoemulgel. Terpene oils (tea tree, neem, citronella) provided synergistic antifungal effects. Optimized formulation showed nanoscale droplet size ( 7.72 nm) with high clarity. Ex vivo permeation and CLSM confirmed 2-fold higher penetration vs. marketed cream. Antifungal efficacy significantly greater than commercial cream with proven safety.